Positioning vehicles to improve quality of observations at intersections

ABSTRACT

Disclosed herein are methods and apparatus for controlling autonomous vehicles utilizing maps that include visibility information. A map is stored at a computing device associated with a vehicle. The vehicle is configured to operate in an autonomous mode that supports a plurality of driving behaviors. The map includes information about a plurality of roads, a plurality of features, and visibility information for at least a first feature in the plurality of features. The computing device queries the map for visibility information for the first feature at a first position. The computing device, in response to querying the map, receives the visibility information for the first feature at the first position. The computing device selects a driving behavior for the vehicle based on the visibility information. The computing device controls the vehicle in accordance with the selected driving behavior.

BACKGROUND

Autonomous vehicles use various computing systems to aid in transportingpassengers from one location to another. Some autonomous vehicles mayrequire some initial input or continuous input from an operator, such asa pilot, driver, or passenger. Other systems, for example autopilotsystems, may be used only when the system has been engaged, whichpermits the operator to switch from a manual mode (where the operatorexercises a high degree of control over the movement of the vehicle) toan autonomous mode (where the vehicle essentially drives itself) tomodes that lie somewhere in between.

SUMMARY

In one aspect, a method is provided. A map is stored at a computingdevice associated with a vehicle. The vehicle is configured to operatein an autonomous operation mode that supports a plurality of drivingbehaviors. The map includes information about a plurality of roads, aplurality of features, and visibility information for at least a firstfeature in the plurality of features. The computing device queries themap for visibility information for the first feature at a firstposition. The computing device, in response to querying the map,receives the visibility information for the first feature at the firstposition. The computing device selects a driving behavior for thevehicle based on the visibility information. The computing devicecontrols the vehicle in accordance with the selected driving behavior.

In another aspect, an article of manufacture is provided. The article ofmanufacture includes a non-transitory computer-readable storage mediumhaving instructions stored thereon that, when executed by a processor,cause the processor to perform functions. The functions include: (a)storing a map for a vehicle, where the vehicle is configured to operatein an autonomous operation mode that supports a plurality of drivingbehaviors, and where the map comprises information about a plurality ofroads, a plurality of features, and visibility information for at leasta first feature in the plurality of features, (b) querying the map forvisibility information for the first feature at a first position, (c) inresponse to the query, receiving the visibility information for thefirst feature at the first position, (d) selecting a driving behaviorfor the vehicle based on the visibility information, and (e) controllingthe vehicle using the computing device in accordance with the selecteddriving behavior.

In still another aspect, a computing device is provided. The computingdevice includes a processor and a non-transitory computer readablemedium having stored thereon instructions that, when executed by theprocessor, cause the computing device to perform functions. Thefunctions include: (a) storing a map for a vehicle in the non-transitorycomputer-readable storage medium, where the vehicle is configured tooperate in an autonomous operation mode that supports a plurality ofdriving behaviors, and where the map comprises information about aplurality of roads, a plurality of features, and visibility informationfor at least a first feature in the plurality of features, (b) queryingthe map for visibility information for the first feature at a firstposition, (c) in response to the query, receiving the visibilityinformation for the first feature at the first position, (d) selecting adriving behavior for the vehicle based on the visibility information,and (e) controlling the vehicle in accordance with the selected drivingbehavior.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a simplified block diagram of an example vehicle, inaccordance with an example embodiment.

FIG. 2 illustrates an example vehicle, in accordance with an exampleembodiment.

FIG. 3 is a flow chart illustrating a method, in accordance with anexample embodiment.

FIG. 4A shows an example map with blocks separated with north-southroads and east-west roads, in accordance with an example embodiment.

FIG. 4B shows a close-up view of an intersection of the example mapshown in FIG. 4A, in accordance with an example embodiment.

DETAILED DESCRIPTION

A computing device can be programmed to be a vehicle control system thatoperates a vehicle autonomously or without a human driver being requiredto direct the vehicle along a route from a start to a destination. Tocontrol the vehicle, the vehicle control system can generate and selectdriving behaviors on the way to the destination. Example drivingbehaviors include one or a combination of: heading left/right, turningleft/right, performing a U-turn, maintaining course and speed,increasing speed, decreasing speed, slowly moving forward, reversingdirection, and stopping. Other driving behaviors are possible as well.

The vehicle control system may be implemented in or may take the form ofa vehicle, such as an automobile. Alternatively, the vehicle controlsystem may be implemented in or take the form of another vehicle, suchas a truck, motorcycle, off-road vehicle, bus, boat, airplane,helicopter, lawn mower, recreational vehicle, amusement park vehicle,farm vehicle, construction vehicle, tram, golf cart, train, or trolley.Other vehicles can possibly be controlled by vehicle control systems aswell.

To autonomously operate the vehicle, the computing device acting as thevehicle control system can estimate the current state of the environmentsurrounding the vehicle based on a number of inputs. A collection ofinputs to the computing device generating and selecting drivingbehaviors can come from sensors on the vehicle. The sensors can provideinformation about features in the vehicle's driving environment, such asother vehicles, traffic signals and signs, directional information,locations, speeds, and acceleration, fuel information, vehicle statusinformation, roads, intersections, lane information, lane boundaries,speed limits, and other features.

Another input to the computing device acting as the vehicle controlsystem can be a map. The map can include location information forfeatures along a road such as traffic signs, signals, buildings, parkinglots, parks/natural areas, additional information about building andother locations along the road, road geometry information, laneinformation, and other information.

The map can include and/or be associated with visibility information forfeatures at various places along a road. Visibility information forlocations along the road can be generated, stored, and retrieved to aidnavigation for the autonomous vehicle. This visibility information canbe stored with the map and/or in data that is otherwise associated withthe map, such as a database that can be queried with map coordinates,e.g., coordinates of an intersection, and return correspondingvisibility information for the map coordinates. Once visibilityinformation is calculated e.g., at a visibility calculation server, thevisibility information can be stored and perhaps distributed to multipleautonomous vehicles. In some embodiments, the autonomous vehicle canquery the visibility server for visibility information and/or mapsduring autonomous vehicle operation.

Intersections can pose particular challenges for autonomous vehicleoperation. Intersections can have traffic lights that are partially orcompletely occluded, or blocked from view, by other vehicles, trees,buildings and other objects. Competing features at intersections, suchas a number of simultaneously visible traffic lights, traffic signs, andnon-traffic lights and signs, can make differentiating traffic-controlfeatures from non-traffic-control features challenging. Also, to safelynavigate an intersection, an autonomous vehicle may view both a currentlane and lanes of one or more cross streets.

To address these particular challenges, the visibility information canindicate, for a given lane at an intersection, which lane(s) providesthe best view(s) of feature(s) at the intersection. The autonomousvehicle can query the map or associated data to get visibilityinformation about an intersection, receive retrieved visibilityinformation about the intersection, and use the retrieved visibilityinformation to generate driving behavior(s), perhaps to improve viewingof feature(s) at the intersection. In some embodiments, the visibilityinformation can include visibility information that indicates visibilityinformation at one or more sub-lane positions within a lane.

For example, suppose the autonomous vehicle is in a right lane of afour-lane road and the visibility information from the map indicatesthat a traffic light at an upcoming intersection is best viewed from aleft lane of the four-lane road. In response, the autonomous vehicle cangenerate driving behaviors to change to the left lane when safelypossible. In some embodiments, the map can store a respective score foreach valid position leading up to the intersection based on how wellthat position allows the vehicle to observe the features of theintersection. The score can be a combined score for a set of features atthe intersection, or can include separate scores for each feature at theintersection.

By computing and storing visibility information ahead of time, theautonomous vehicle can simply query the map and/or associated data whilenavigating rather than needing to perform visibility calculationson-the-fly. Thus, computing resources that may have been used tocalculate visibility information in real-time can be saved by usingstored visibility information. Further, these computing resources can bebeing saved while approaching and/or navigating through an intersection,likely to be a critical time of autonomous vehicle operation duringwhich computing resources may be heavily loaded.

Referring now to the Figures, FIG. 1 is a simplified block diagram of anexample vehicle 100, in accordance with an example embodiment.Components coupled to or included in the vehicle 100 may include apropulsion system 102, a sensor system 104, a control system 106,peripherals 108, a power supply 110, a computing device 111, and a userinterface 112. The computing device 111 may include a processor 113, anda memory 114. The memory 114 may include instructions 115 executable bythe processor 113, and may also store map data 116. Components of thevehicle 100 may be configured to work in an interconnected fashion witheach other and/or with other components coupled to respective systems.For example, the power supply 110 may provide power to all thecomponents of the vehicle 100. The computing device 111 may beconfigured to receive information from and control the propulsion system102, the sensor system 104, the control system 106, and the peripherals108. The computing device 111 may be configured to generate a display ofimages on and receive inputs from the user interface 112.

Map data 116 can include information for one or more roads and featuresalong the roads. The road information can include locations that theroad travels through, connecting roads, intersections, road names and/ornumbers, road geometry information, road size (e.g., one lane, twolanes, etc.), lane information, and other information. Features alongthe road can include but are not limited to any combination of trafficsigns, traffic signals, other signs and signals, buildings, parkinglots, parks/natural areas, historical markers, amenities,points-of-interest, businesses, and additional information aboutlocations along the road.

Map data 116 can include and/or be associated with visibilityinformation 117 for features at various places along a road. Visibilityinformation 117 can be generated, stored, and retrieved to aidnavigation for the autonomous vehicle. Visibility information 117 can bestored with map data 116, with data that is otherwise associated withthe map, such as a database that can be queried with map coordinates foran intersection and return the corresponding visibility information forthe intersection. Once visibility information is calculated e.g., at avisibility calculation server, the visibility information can be storedand distributed to multiple autonomous vehicles. In some embodiments,the autonomous vehicle can query the visibility server for visibilityinformation and/or maps during autonomous vehicle operation.

In other examples, the vehicle 100 may include more, fewer, or differentsystems, and each system may include more, fewer, or differentcomponents. Additionally, the systems and components shown may becombined or divided in any number of ways.

The propulsion system 102 may may be configured to provide poweredmotion for the vehicle 100. As shown, the propulsion system 102 includesan engine/motor 118, an energy source 120, a transmission 122, andwheels/tires 124. The propulsion system 102 may additionally oralternatively include components other than those shown.

The engine/motor 118 may be or include any combination of an internalcombustion engine, an electric motor, a steam engine, and a Stirlingengine. Other motors and engines are possible as well. In some examples,the propulsion system 102 could include multiple types of engines and/ormotors. For instance, a gas-electric hybrid car could include a gasolineengine and an electric motor. Other examples are possible.

The energy source 120 may be a source of energy that powers theengine/motor 118 in full or in part. That is, the engine/motor 118 maybe configured to convert the energy source 120 into mechanical energy.Examples of energy sources 120 include gasoline, diesel, otherpetroleum-based fuels, propane, other compressed gas-based fuels,ethanol, solar panels, batteries, and other sources of electrical power.The energy source(s) 120 could additionally or alternatively include anycombination of fuel tanks, batteries, capacitors, and/or flywheels. Insome examples, the energy source 120 may provide energy for othersystems of the vehicle 100 as well.

The transmission 122 may be configured to transmit mechanical power fromthe engine/motor 118 to the wheels/tires 124. To this end, thetransmission 122 may include a gearbox, clutch, differential, driveshafts, and/or other elements. In examples where the transmission 122includes drive shafts, the drive shafts could include one or more axlesthat are configured to be coupled to the wheels/tires 124.

The wheels/tires 124 of vehicle 100 could be configured in variousformats, including a unicycle, bicycle/motorcycle, tricycle, orcar/truck four-wheel format. Other wheel/tire formats are possible aswell, such as those including six or more wheels. The wheels/tires 124of vehicle 100 may be configured to rotate differentially with respectto other wheels/tires 124. In some examples, the wheels/tires 124 mayinclude at least one wheel that is fixedly attached to the transmission122 and at least one tire coupled to a rim of the wheel that could makecontact with the driving surface. The wheels/tires 124 may include anycombination of metal and rubber, or combination of other materials.

The sensor system 104 may include a number of sensors configured tosense information about an environment in which the vehicle 100 islocated. As shown, the sensors of the sensor system include a GlobalPositioning System (GPS) module 126, an inertial measurement unit (IMU)128, a RADAR unit 130, a laser rangefinder and/or LIDAR unit 132, acamera 134, and actuators 136 configured to modify a position and/ororientation of the sensors. The sensor system 104 may include additionalsensors as well, including, for example, sensors that monitor internalsystems of the vehicle 100 (e.g., an oxygen monitor, a fuel gauge, anengine oil temperature, etc.). The sensor system 104 may additionally oralternatively include components other than those shown. Other sensorsare possible as well

The GPS module 126 may be any sensor configured to estimate a geographiclocation of the vehicle 100. To this end, the GPS module 126 may includea transceiver configured to estimate a position of the vehicle 100 withrespect to the Earth, based on satellite-based positioning data. In anexample, the computing device 111 may be configured to use the GPSmodule 126 in combination with the map data 116 to estimate a locationof a lane boundary on road on which the vehicle 100 may be travellingon. The GPS module 126 may take other forms as well.

The IMU 128 may be any combination of sensors configured to senseposition and orientation changes of the vehicle 100 based on inertialacceleration. In some examples, the combination of sensors may include,for example, accelerometers and gyroscopes. Other combinations ofsensors are possible as well.

The RADAR 130 unit may be any sensor configured to sense objects in theenvironment in which the vehicle 100 is located using radio signals. Insome examples, in addition to sensing the objects, the RADAR unit 130may additionally be configured to sense the speed and/or direction ofmotion of the objects.

Similarly, the laser rangefinder or LIDAR unit 132 may be any sensorconfigured to sense objects in the environment in which the vehicle 100is located using lasers. In particular, the laser rangefinder or LIDARunit 132 may include a laser source and/or laser scanner configured toemit a laser and a detector configured to detect reflections of thelaser. The laser rangefinder or LIDAR 132 may be configured to operatein a coherent (e.g., using heterodyne detection) or an incoherentdetection mode.

The camera 134 may be any camera (e.g., a still camera, a video camera,etc.) configured to capture images of the environment in which thevehicle 100 is located. To this end, the camera may take any of theforms described above.

The control system 106 may be configured to control operation of thevehicle 100 and its components. To this end, the control system 106 mayinclude a steering unit 138, a throttle 140, a brake unit 142, a sensorfusion algorithm 144, a computer vision system 146, a navigation orpathing system 148, and an obstacle avoidance system 150.

The steering unit 138 may be any combination of mechanisms configured toadjust the heading or direction of the vehicle 100. The throttle 140 maybe any combination of mechanisms configured to control the operatingspeed and acceleration of the engine/motor 118 and, in turn, the speedand acceleration of the vehicle 100.

The brake unit 142 may be any combination of mechanisms configured todecelerate the vehicle 100. For example, the brake unit 142 may usefriction to slow the wheels/tires 124. As another example, the brakeunit 142 may be configured to be regenerative and convert the kineticenergy of the wheels/tires 124 to electric current. The brake unit 142may take other forms as well.

The sensor fusion algorithm 144 may include an algorithm (or a computerprogram product storing an algorithm) executable by the computing device111, for example. The sensor fusion algorithm 144 may be configured toaccept data from the sensor system 104 as an input. The data mayinclude, for example, data representing information sensed at thesensors of the sensor system 104. The sensor fusion algorithm 144 mayinclude, for example, a Kalman filter, a Bayesian network, or anotheralgorithm. The sensor fusion algorithm 144 may further be configured toprovide various assessments based on the data from the sensor system104, including, for example, evaluations of individual objects and/orfeatures in the environment in which the vehicle 100 is located,evaluations of particular situations, and/or evaluations of possibleimpacts based on particular situations. Other assessments are possibleas well

The computer vision system 146 may be any system configured to processand analyze images captured by the camera 134 in order to identifyobjects and/or features in the environment in which the vehicle 100 islocated, including, for example, lane information, traffic signals andobstacles. To this end, the computer vision system 146 may use an objectrecognition algorithm, a Structure from Motion (SFM) algorithm, videotracking, or other computer vision techniques. In some examples, thecomputer vision system 146 may additionally be configured to map theenvironment, track objects, estimate the speed of objects, etc.

The navigation and pathing system 148 may be any system configured todetermine a driving path for the vehicle 100. The navigation and pathingsystem 148 may additionally be configured to update the driving pathdynamically while the vehicle 100 is in operation. In some examples, thenavigation and pathing system 148 may be configured to incorporate datafrom the sensor fusion algorithm 144, the GPS module 126, and one ormore predetermined maps so as to determine the driving path for thevehicle 100.

The obstacle avoidance system 150 may be any system configured toidentify, evaluate, and avoid or otherwise negotiate obstacles in theenvironment in which the vehicle 100 is located.

The control system 106 may additionally or alternatively includecomponents other than those shown.

Peripherals 108 may be configured to allow the vehicle 100 to interactwith external sensors, other vehicles, and/or a user. To this end, theperipherals 108 may include, for example, a wireless communicationsystem 152, a touchscreen 154, a microphone 156, and/or a speaker 158.

The wireless communication system 152 may be any system configured to bewirelessly coupled to one or more other vehicles, sensors, or otherentities, either directly or via a communication network. To this end,the wireless communication system 152 may include an antenna and achipset for communicating with the other vehicles, sensors, or otherentities either directly or over an air interface. The chipset orwireless communication system 152 in general may be arranged tocommunicate according to one or more other types of wirelesscommunication (e.g., protocols) such as Bluetooth, communicationprotocols described in IEEE 802.11 (including any IEEE 802.11revisions), cellular technology (such as GSM, CDMA, UMTS, EV-DO, WiMAX,or LTE), Zigbee, dedicated short range communications (DSRC), and radiofrequency identification (RFID) communications, among otherpossibilities. The wireless communication system 152 may take otherforms as well.

The touchscreen 154 may be used by a user to input commands to thevehicle 100. To this end, the touchscreen 154 may be configured to senseat least one of a position and a movement of a user's finger viacapacitive sensing, resistance sensing, or a surface acoustic waveprocess, among other possibilities. The touchscreen 154 may be capableof sensing finger movement in a direction parallel or planar to thetouchscreen surface, in a direction normal to the touchscreen surface,or both, and may also be capable of sensing a level of pressure appliedto the touchscreen surface. The touchscreen 154 may be formed of one ormore translucent or transparent insulating layers and one or moretranslucent or transparent conducting layers. The touchscreen 154 maytake other forms as well

The microphone 156 may be configured to receive audio (e.g., a voicecommand or other audio input) from a user of the vehicle 100. Similarly,the speakers 158 may be configured to output audio to the user of thevehicle 100.

The peripherals 108 may additionally or alternatively include componentsother than those shown.

The power supply 110 may be configured to provide power to some or allof the components of the vehicle 100. To this end, the power supply 110may include, for example, a rechargeable lithium-ion or lead-acidbattery. In some examples, one or more banks of batteries could beconfigured to provide electrical power. Other power supply materials andconfigurations are possible as well. In some examples, the power supply110 and energy source 120 may be implemented together, as in someall-electric cars.

The processor 113 included in the computing device 111 may comprise oneor more general-purpose processors and/or one or more special-purposeprocessors. To the extent the processor 113 includes more than oneprocessor; such processors could work separately or in combination. Thecomputing device 111 may be configured to control functions of thevehicle 100 based on input received through the user interface 112, forexample.

The memory 114, in turn, may comprise one or more volatile and/or one ormore non-volatile storage components, such as optical, magnetic, and/ororganic storage, and the memory 114 may be integrated in whole or inpart with the processor 113. The memory 114 may contain the instructions115 (e.g., program logic) executable by the processor 113 to executevarious vehicle functions.

The components of the vehicle 100 could be configured to work in aninterconnected fashion with other components within and/or outside theirrespective systems. To this end, the components and systems of thevehicle 100 may be communicatively linked together by a system bus,network, and/or other connection mechanism (not shown).

Further, while each of the components and systems are shown to beintegrated in the vehicle 100, in some examples, one or more componentsor systems may be removably mounted on or otherwise connected(mechanically or electrically) to the vehicle 100 using wired orwireless connections.

The vehicle 100 may include one or more elements in addition to orinstead of those shown. For example, the vehicle 100 may include one ormore additional interfaces and/or power supplies. Other additionalcomponents are possible as well. In these examples, the memory 114 mayfurther include instructions executable by the processor 113 to controland/or communicate with the additional components.

FIG. 2 illustrates an example vehicle 200, in accordance with anembodiment. In particular, FIG. 2 shows a Right Side View, Front View,Back View, and Top View of the vehicle 200. Although vehicle 200 isillustrated in FIG. 2 as an automobile, other examples are possible. Forinstance, the vehicle 200 could represent a truck, motorcycle, off-roadvehicle, bus, boat, airplane, helicopter, lawn mower, recreationalvehicle, amusement park vehicle, farm vehicle, construction vehicle,tram, golf cart, train, trolley, or some other vehicle. As shown, thevehicle 200 includes a first sensor unit 202, a second sensor unit 204,a third sensor unit 206, a wireless communication system 208, and acamera 210. In some embodiments, vehicle 200 can include one or moreother components in addition to or instead of those shown.

Each of the first, second, and third sensor units 202-206 may includeany combination of global positioning system sensors, inertialmeasurement units, RADAR units, laser rangefinders, LIDAR units,cameras, lane detection sensors, and acoustic sensors. Other types ofsensors are possible as well.

While the first, second, and third sensor units 202 are shown to bemounted in particular locations on the vehicle 200, in some examples thesensor unit 202 may be mounted elsewhere on the vehicle 200, eitherinside or outside the vehicle 200. Further, while only three sensorunits are shown, in some examples more or fewer sensor units may beincluded in the vehicle 200.

In some examples, one or more of the first, second, and third sensorunits 202-206 may include one or more movable mounts on which thesensors may be movably mounted. The movable mount may include, forexample, a rotating platform. Sensors mounted on the rotating platformcould be rotated so that the sensors may obtain information from eachdirection around the vehicle 200. Alternatively or additionally, themovable mount may include a tilting platform. Sensors mounted on thetilting platform could be tilted within a particular range of anglesand/or azimuths so that the sensors may obtain information from avariety of angles. The movable mount may take other forms as well.

Further, in some examples, one or more of the first, second, and thirdsensor units 202-206 may include one or more actuators configured toadjust the position and/or orientation of sensors in the sensor unit bymoving the sensors and/or movable mounts. Example actuators includemotors, pneumatic actuators, hydraulic pistons, relays, solenoids, andpiezoelectric actuators. Other actuators are possible as well.

The wireless communication system 208 may be any system configured towirelessly couple to one or more other vehicles, sensors, or otherentities, either directly or via a communication network as describedabove with respect to the wireless communication system 152 in FIG. 1.While the wireless communication system 208 is shown to be positioned ona roof of the vehicle 200, in other examples the wireless communicationsystem 208 could be located, fully or in part, elsewhere.

The camera 210 may be any camera (e.g., a still camera, a video camera,etc.) configured to capture images of the environment in which thevehicle 200 is located. To this end, the camera 210 may be configured todetect visible light, or may be configured to detect light from otherportions of the spectrum, such as infrared or ultraviolet light, orx-rays. Other types of cameras are possible as well. The camera 210 maybe a two-dimensional detector, or may have a three-dimensional spatialrange. In some examples, the camera 210 may be, for example, a rangedetector configured to generate a two-dimensional image indicating adistance from the camera 210 to a number of points in the environment.To this end, the camera 210 may use one or more range detectingtechniques. For example, the camera 210 may use a structured lighttechnique in which the vehicle 200 illuminates an object in theenvironment with a predetermined light pattern, such as a grid orcheckerboard pattern and uses the camera 210 to detect a reflection ofthe predetermined light pattern off the object. Based on distortions inthe reflected light pattern, the vehicle 200 may determine the distanceto the points on the object. The predetermined light pattern maycomprise infrared light, or light of another wavelength.

As another example, the camera 210 may use a laser scanning technique inwhich the vehicle 200 emits a laser and scans across a number of pointson an object in the environment. While scanning the object, the vehicle200 uses the camera 210 to detect a reflection of the laser off theobject for each point. Based on a length of time it takes the laser toreflect off the object at each point, the vehicle 200 may determine thedistance to the points on the object.

As yet another example, the camera 210 may use a time-of-flighttechnique in which the vehicle 200 emits a light pulse and uses thecamera 210 to detect a reflection of the light pulse off an object at anumber of points on the object. In particular, the camera 210 mayinclude a number of pixels, and each pixel may detect the reflection ofthe light pulse from a point on the object. Based on a length of time ittakes the light pulse to reflect off the object at each point, thevehicle 200 may determine the distance to the points on the object. Thelight pulse may be a laser pulse, for example. Other range detectingtechniques are possible as well, including stereo triangulation,sheet-of-light triangulation, interferometry, and coded aperturetechniques, among others. The camera 210 may take other forms as well.

In some examples, the camera 210 may include a movable mount and/or anactuator configured to adjust the position and/or orientation of thecamera 210. While FIG. 2 shows camera 210 mounted inside a frontwindshield of the vehicle 200, in other examples the camera 210 may bemounted elsewhere on the vehicle 200, either inside or outside thevehicle 200.

A control system of the vehicle 200 may be configured to control thevehicle 200 in accordance with a given driving behavior from amongmultiple possible driving behaviors. The control system may beconfigured to receive information from sensors coupled to the vehicle200 (on or off the vehicle 200), select a driving behavior based on theinformation, and control the vehicle 200 in accordance with the selecteddriving behavior. The control system further may be configured tocontinuously monitor the information received from the sensors tocontinuously evaluate driving conditions and also may be configured tomodify the driving behavior or select another driving behavior based onchanges in the driving conditions.

FIG. 3 is a flow chart illustrating an example method 300. In thisexample, method 300 begins at block 310, where a map can be stored at acomputing device associated with a vehicle. The vehicle can beconfigured to operate in an autonomous operation mode that supports aplurality of driving behaviors. The map can include information about aplurality of roads, a plurality of features, and visibility informationfor at least a first feature in the plurality of features.

In some embodiments, the first position can be associated with anintersection of at least two roads in the plurality of roads. In otherembodiments, the first position can be associated with a lane positionon a road in the plurality of roads, where the lane position isassociated with a lane of the road. In particular of these otherembodiments, the first position can be associated with both the laneposition and a sub-lane position within the lane of the road.

In still other embodiments, the first feature is associated with a firsttraffic control device. In particular of the still other embodiments,the first traffic control device is further associated with a secondfeature, where the first feature includes a traffic light and the secondfeature includes a turn-control signal.

At block 320, the computing device can query the map for visibilityinformation for the first feature at a first position. In someembodiments, the visibility information can be stored with the map,while in other embodiments, the visibility information can be stored indata associated with the map, such as a visibility-information databaseconfigured to be queried based on map coordinates, or other reference(s)to the map.

In still other embodiments, the visibility information can include avisibility index, perhaps a numerical visibility index. In particular ofthe still other embodiments, the first position can be associated with aplurality of sub-positions, and where the visibility index can includean ordering of best-to-worst sub-positions at the first location.

In yet other embodiments, querying the map for visibility informationfor the first feature can include querying the map for visibilityinformation for the first feature based on a time of day. In even otherembodiments, the visibility information of the first feature at thefirst position can be based on an occluding object associated with thefirst feature.

At block 330, the computing device can, in response to querying the map,receive the visibility information for the first feature at the firstposition.

At block 340, the computing device can select a driving behavior for thevehicle based on the visibility information. Example driving behaviorsinclude but are not limited to, one or a combination of: headingleft/right, turning left/right, performing a U-turn, maintaining courseand speed, increasing speed, decreasing speed, slowly moving forward,reversing direction, and stopping.

At block 350, the computing device can control the vehicle in accordancewith the selected driving behavior.

FIG. 4A shows an example map 400 with nine blocks 450-466 separated bynorth-south roads Main St. 414 and Oak St. 416 and east-west roads OneWay St. 410 and Two-Way St. 412. The north-south roads and east-westroads make four intersections 422, 424, 426, and 428. Intersection 422between One-Way St. 410 and Main St. 414 includes several trafficlights, shown with boxes surrounding the letter “L” on FIG. 4A.Intersections 424, 426, and 428 respectively have three, two, and fourstop signs for traffic control. Each stop sign is shown on FIG. 4A as anoctagon surrounding the letter “S”.

The autonomous vehicle can use visibility information to position itselfto observe key information at each intersection. For example, visibilityinformation can include data at various lane positions that indicateswhich lane position provides the best view of features at eachintersection. These features can include but are not limited to anycombination of traffic signs, traffic signals, other signs and signals,buildings, parking lots, parks/natural areas, historical markers,amenities, points-of-interest, businesses, and additional informationabout locations associated with the intersection. Other features arepossible as well.

This visibility information can be stored with the map and/or in datathat is otherwise associated with the map, such as a database that canbe queried with map coordinates for an intersection and return thecorresponding visibility information for the intersection. By computingand storing visibility information ahead of time, the autonomous vehiclecan simply query the map and/or associate data while navigating ratherthan needing to perform visibility calculations on-the-fly to estimatethe best lane position for visibility and perhaps whether or not acurrent lane position will suffice.

FIG. 4B shows a close-up view of intersection 422 of map 400.Intersection 422 is an intersection between One-way St. 410 and Main St.414, with five traffic lights 432, 434, 436, 438, and 440. Light 434 canbe further sub-divided into two features: vertical traffic lights 434 aand right-turn signal 434 b. FIG. 4B shows that Main St. 414 has 4lanes: Southbound Lanes 1 and 2, and Northbound Lanes 1 and 2.

In some embodiments, a position within a lane of a road can be termed tobe a lane position of the road. For example, Main St. has four lanepositions: Southbound Lane 1, Southbound Lane 2, Northbound Lane 1, andNorthbound Lane 2. Further, a lane position can be divided intosub-positions. FIG. 4B shows various lane positions and sub-lanepositions on the south side of intersection 422. Lane Position (LP)441.10 shows a position in Northbound Lane 1 approximately 1/10^(th) ofthe way between the left-side lane marker (shown as a thick dark line onFIG. 4B) and the right-side lane marker separating Northbound Lanes 1and 2. LPs 441.50 and 441.90 respectively show positions in NorthboundLane 1 approximately halfway and approximately 9/10^(th) of the waybetween the left-side and right-side lane markers. Similarly LPs 442.10,442.50, and 442.90 respectively show positions in Northbound Lane 2approximately 1/10^(th), halfway, and 9/10^(th) of the way between theleft-side and right-side lane markers.

Visibility information can be calculated for each feature visible ateach lane position and sub-lane position. At LP 441.10, lookingnorthward, visible features include One-Way St. 410, Main St. 414,intersection 422, lights 432, 434 a, 434 b, 436, 438, 440, trees 444,446, 448, and blocks 450 and 452.

Example visibility information for feature 434 b at lane positions441.10-442.90 is shown in Table 1 below. The visibility informationshown in Table 1 is expressed as a visibility index expressing avisibility value from 0 (cannot be seen) to 10 (wholly visible).

TABLE 1 Lane Position Visibility Index (LP) of Feature 434b 441.10 9.9441.50 9.8 441.90 9.5 442.10 9.4 442.50 9.3 442.90 9.0

Upon retrieving the stored visibility information, the autonomousvehicle can move into the most-visible lane on its approach to theintersection, if feasible based on traffic conditions, desired routing,and perhaps other criteria. Once the autonomous vehicle gets in themost-visible lane, the autonomous vehicle can get a more reliableestimate of key information, such as a state of the traffic light (e.g.,red, green or yellow) and locations of other vehicles.

At some positions, some or all features can be occluded, or blocked,from view by an occluding object. FIG. 4B shows that tree 448 canocclude a view of light 438 from lane positions 442.10 through 442.90.In contrast, at position 441.10, light 438 may be partially visiblebetween trees 446 and 448. In these cases, a visibility index atposition 442.90 of feature light 438 can be 0, with an indication ofocclusion by trees 448. Then, if tree 448 is no longer near light 438,such as being downed by natural or human causes, the visibility index oflight 438 can be updated to be greater than zero and the indication ofocclusion can be removed.

In some embodiments, the map and/or associated data may store where in aparticular lane (e.g., slightly to left or right) the vehicle should tryto be in order to get better information regarding the intersection,etc. One possible implementation is to store a score associated withevery possible (legal) position leading up to the intersection (in alllanes) based on how well that position allows the vehicle to observe thekey features of the intersection. Another possible implementation canstore a “relative score”; e.g., visibility for feature F at location Limproves as the vehicle moves east, visibility for feature F1 atlocation L1 degrades as the vehicle moves west or south, visibility forfeature F2 at location L2 degrades as the vehicle moves north towardoccluding object OO but improves as the vehicle passes and continues tomove north past occluding object OO. In some embodiments, visibilityinformation can be or include a binary indication of whether a feature Fis visible or not visible at a particular location. In otherembodiments, the visibility information can be stored as an ordered listof best-to-worst locations.

In particular embodiments, visibility information can be stored forpoints along the road as well. For example, the visibility informationcan be stored at distance-based intervals along the road e.g., every 200yards, or 0.5 km, at points where the visibility changes arelatively-large amount such as going into and/or leaving a bend in aroad, as features become visible along the road, and/or based on othercriteria.

A visibility calculation can take several criteria into account: time ofday, temporary road hazards (e.g., effects of road construction),occluding features such as nearby-vegetation and bright lights, roadconditions, traffic patterns, weather-related conditions, and/or othercriteria. For example, a feature F4 may be visible at a location L4traveling in a direction D during daylight and nighttime hours, but F4is not readily visible at L4 traveling in direction D from a time aboutone half hour before sunset until dusk, due to sensors being in directsunlight at L4 looking in direction D at F4. As such, the visibilityinformation can indicate that visibility of F4 from L4 is relatively lowduring a time interval.

Stored visibility information may include environmental lightinginformation, such as sunset/sunrise information, moon-phase information,etc. For example, use of a sunset/sunrise table can determine that onMar. 2, 2012 for L4 in “Mountain View, Calif.”, “dawn” is at 6:11 AM,“sunrise” is at 6:37 AM, “sunset” is at 6:04 PM, and “dusk” is at 6:30PM. Then, based on the sunset/sunrise data, a visibility calculation candetermine a not-visible interval for F4 at L4, in Mountain View, Calif.,traveling in direction D from a time about one half hour before sunset,or about 5:34 PM, until dusk, or 6:30 PM.

As another example, a feature F5 that is relatively far from a road Rthat is visible during the day may not be visible while driving at nightand using headlights. As such, the visibility information can indicatethat visibility of F5 from various locations along R is relatively lowduring night hours and is relatively high during day hours. If night orday hours are defined based on sunset and sunrise, then the visibilitydata can use the environmental lighting information to determinevisibility time intervals for F5.

Suppose that “day hours” are defined at “sunrise to sunset” and nighthours are defined as “one minute after sunset to one minute beforesunrise”. Using these definitions, if F5 is also in Mountain View,Calif., then on Mar. 2, 2012, F5 has a visible time interval during dayhours between 6:37 AM to 6:04 PM, and a not-visible time interval duringnight hours from 6:05 PM until 6:35 AM on Mar. 3, 2012, as sunrise inMountain View, Calif. on Mar. 3, 2012 is at 6:36 AM.

As yet another example, certain features along a truck route can beoccluded by large trucks that typically operate during business hours,but those same features are typically not occluded by vehicles operatingalong the truck route during non-business hours. As such, time of dayvisibility information can further be based on typically traffic flowsfor that time of day.

The above detailed description describes various features and functionsof the disclosed systems, devices, and methods with reference to theaccompanying figures. In the figures, similar symbols typically identifysimilar components, nless context dictates otherwise. The illustrativeembodiments described in the detailed description, figures, and claimsare not meant to be limiting. Other embodiments can be utilized, andother changes can be made, without departing from the spirit or scope ofthe subject matter presented herein. It will be readily understood thatthe aspects of the present disclosure, as generally described herein,and illustrated in the figures, can be arranged, substituted, combined,separated, and designed in a wide variety of different configurations,all of which are explicitly contemplated herein.

With respect to any or all of the ladder diagrams, scenarios, and flowcharts in the figures and as discussed herein, each block and/orcommunication may represent a processing of information and/or atransmission of information in accordance with example embodiments.Alternative embodiments are included within the scope of these exampleembodiments. In these alternative embodiments, for example, functionsdescribed as blocks, transmissions, communications, requests, responses,and/or messages may be executed out of order from that shown ordiscussed, including substantially concurrent or in reverse order,depending on the functionality involved. Further, more or fewer blocksand/or functions may be used with any of the ladder diagrams, scenarios,and flow charts discussed herein, and these ladder diagrams, scenarios,and flow charts may be combined with one another, in part or in whole.

A block that represents a processing of information may correspond tocircuitry that can be configured to perform the specific logicalfunctions of a herein-described method or technique. Alternatively oradditionally, a block that represents a processing of information maycorrespond to a module, a segment, or a portion of program code(including related data). The program code may include one or moreinstructions executable by a processor for implementing specific logicalfunctions or actions in the method or technique. The program code and/orrelated data may be stored on any type of computer readable medium suchas a storage device including a disk or hard drive or other storagemedium.

The computer readable medium may also include non-transitory computerreadable media such as computer-readable media that stores data forshort periods of time like register memory, processor cache, and randomaccess memory (RAM). The computer readable media may also includenon-transitory computer readable media that stores program code and/ordata for longer periods of time, such as secondary or persistent longterm storage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. The computerreadable media may also be any other volatile or non-volatile storagesystems. A computer readable medium may be considered a computerreadable storage medium, for example, or a tangible storage device.

Moreover, a block that represents one or more information transmissionsmay correspond to information transmissions between software and/orhardware modules in the same physical device. However, other informationtransmissions may be between software modules and/or hardware modules indifferent physical devices.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

What is claimed is:
 1. A method, comprising: storing a map at acomputing device associated with a vehicle, wherein the vehicle isconfigured to operate in an autonomous operation mode that supports aplurality of driving behaviors, and wherein the map comprisesinformation about a plurality of roads, a plurality of features, andvisibility information for at least a first feature in the plurality offeatures; querying the map, using the computing device, for visibilityinformation for the first feature at a first position of the vehicle,wherein the first position is associated with an intersection of atleast two roads in the plurality of roads, wherein the visibilityinformation comprises visibility information for a plurality ofsub-positions of the vehicle, wherein at least one sub-position of theplurality of sub-positions is associated with a designated sub-laneposition of a lane position of a lane of one road of the at least tworoads, wherein the lane position is specified with respect to aplurality of sub-lane positions, the plurality of sub-lane positionscomprising the designated sub-lane position, and wherein each sub-laneposition in the plurality of sub-lane positions specifies a positionwithin a width of the lane; in response to querying the map, thecomputing device receiving the visibility information for the firstfeature at the first position of the vehicle; selecting a drivingbehavior for the vehicle using the computing device based on thevisibility information; and controlling the vehicle using the computingdevice in accordance with the selected driving behavior.
 2. The methodof claim 1, wherein the one road of the at least two roads comprises aplurality of lanes, and for each lane in the plurality of lanes: theplurality of sub-positions comprises a sub-position associated with alane position of the lane.
 3. The method of claim 1, wherein the firstposition of the vehicle is further associated with both the laneposition and a sub-lane position of the lane position.
 4. The method ofclaim 1, wherein the first feature is associated with a first trafficcontrol device.
 5. The method of claim 4, wherein the first trafficcontrol device is further associated with a second feature, wherein thefirst feature comprises a traffic light, and wherein the second featurecomprises a turn-control signal.
 6. The method of claim 1, wherein thevisibility information comprises a visibility index.
 7. The method ofclaim 6, wherein the first position of the vehicle is associated withthe plurality of sub-positions, and wherein the visibility indexcomprises an ordering of best-to-worst sub-positions at the firstposition of the vehicle.
 8. The method of claim 1, wherein querying themap for visibility information for the first feature comprises queryingthe map for visibility information for the first feature based on a timeof day.
 9. The method of claim 1, wherein the visibility information ofthe first feature at the first position of the vehicle is based on anoccluding object associated with the first feature.
 10. An article ofmanufacture including a non-transitory computer-readable storage mediumhaving instructions stored thereon that, when executed by a processor,cause the processor to perform functions comprising: storing a map for avehicle, wherein the vehicle is configured to operate in an autonomousoperation mode that supports a plurality of driving behaviors, andwherein the map comprises information about a plurality of roads, aplurality of features, and visibility information for at least a firstfeature in the plurality of features; querying the map for visibilityinformation for the first feature at a first position of the vehicle,wherein the first position is associated with an intersection of atleast two roads in the plurality of roads, wherein the visibilityinformation comprises visibility information for a plurality ofsub-positions of the vehicle, wherein at least one sub-position of theplurality of sub-positions is associated with a designated sub-laneposition of a lane position of a lane of one road of the at least tworoads, wherein the lane position is specified with respect to aplurality of sub-lane positions, the plurality of sub-lane positionscomprising the designated sub-lane position, and wherein each sub-laneposition in the plurality of sub-lane positions specifies a positionwithin a width of the lane; in response to the query, receiving thevisibility information for the first feature at the first position ofthe vehicle; selecting a driving behavior for the vehicle based on thevisibility information; and controlling the vehicle using the computingdevice in accordance with the selected driving behavior.
 11. The articleof manufacture of claim 10, wherein the one road of the at least tworoads comprises a plurality of lanes, and for each lane in the pluralityof lanes: the plurality of sub-positions comprises a sub-positionassociated with a lane position of the lane.
 12. The article ofmanufacture of claim 10, wherein the lane position is configured to bedivided into a plurality of sub-lane positions.
 13. The article ofmanufacture of claim 12, wherein the first position of the vehicle isfurther associated with both the lane position and a sub-lane positionof the lane position.
 14. The article of manufacture of claim 10,wherein the first feature is associated with a first traffic controldevice.
 15. The article of manufacture of claim 14, wherein the firsttraffic control device is further associated with a second feature,wherein the first feature comprises a traffic light, and wherein thesecond feature comprises a turn-control signal.
 16. The article ofmanufacture of claim 10, wherein the visibility information comprises avisibility index.
 17. The article of manufacture of claim 16, whereinthe first position of the vehicle is associated with the plurality ofsub-positions, and wherein the visibility index comprises an ordering ofbest-to-worst sub-positions at the first position of the vehicle. 18.The article of manufacture of claim 16, wherein the visibility indexcomprises a visibility index of the first feature based on a time ofday.
 19. A computing device, comprising: a processor; and anon-transitory computer-readable storage medium, configured to storeinstructions that, when executed by the processor, cause the processorto computing device to perform functions, comprising: storing a map fora vehicle in the non-transitory computer-readable storage medium,wherein the vehicle is configured to operate in an autonomous operationmode that supports a plurality of driving behaviors, and wherein the mapcomprises information about a plurality of roads, a plurality offeatures, and visibility information for at least a first feature in theplurality of features, querying the map for visibility information forthe first feature at a first position of the vehicle, wherein the firstposition is associated with an intersection of at least two roads in theplurality of roads, wherein the visibility information comprisesvisibility information for a plurality of sub-positions of the vehicle,wherein at least one sub-position of the plurality of sub-positions isassociated with a designated sub-lane position of a lane position of alane of one road of the at least two roads, wherein the lane position isspecified with respect to a plurality of sub-lane positions, theplurality of sub-lane positions comprising the designated sub-laneposition, and wherein each sub-lane position in the plurality ofsub-lane positions specifies a position within a width of the lane, inresponse to the query, receiving the visibility information for thefirst feature at the first position of the vehicle, selecting a drivingbehavior for the vehicle based on the visibility information, andcontrolling the vehicle in accordance with the selected drivingbehavior.